Modern drilling involves scores of people and multiple inter-connecting activities. Obtaining real-time information about ongoing operations is of paramount importance for safe, efficient drilling. As a result, modern rigs often have thousands of sensors actively measuring numerous parameters related to rig operation, in addition to information about the down-hole drilling environment.
Despite the multitude of sensors on today's rigs, a significant portion of rig activities and sensing problems remain difficult to measure with classical instrumentation, and person-in-the-loop sensing is often utilized in place of automated sensing.
By applying automated, computer-based video interpretation, continuous, robust, and accurate assessment of many different phenomena can be achieved through pre-existing video data without requiring a person-in-the-loop. Automated interpretation of video data is known as computer vision, and recent advances in computer vision technologies have led to significantly improved performance across a wide range of video-based sensing tasks. Computer vision can be used to improve safety, reduce costs and improve efficiency.
As drilling fluid is pumped into the well-bore and back up, it typically carries with it solid material known as drilling cuttings. These cuttings are typically separated from the drilling fluid on an instrument known as a shale shaker or shaker table. The process of separating the cuttings from the fluid may be difficult to monitor using classical instrumentation due to the violent nature of the shaking process. Currently the volume of cuttings is difficult to measure and typically requires man-power to monitor. Knowledge of the total volume and/or approximate volume of the cuttings coming off the shaker table may improve the efficiency, safety, and/or environmental impact of the drilling process.
Additionally, the location and orientation of the fluid front on the shale shaker is an important parameter to the drilling process that may be difficult to measure accurately. Currently this is somewhat difficult to measure and requires man-power to monitor.
Particulate matter that is returned up the well-bore during drilling also contains a great deal of information about the lithology and/or properties of the subsurface, and can give significant insight into the behavior of the well-bore (e.g., indicating cave-ins, or failure to clean). Current drilling operations require human-in-the-loop analysis of these cuttings; a geologist has to go inspect the cuttings on a conveyor belt or other receptacle down-stream from the shale-shakers. This process is time consuming, expensive, and error prone. Classical instrumentation approaches to particle analysis are extremely difficult to design and implement—the sizes, shapes, and consistencies of cuttings prohibit most automated mechanical handling and measurement. In contrast, significant information can be obtained from visual analysis of the particles on the shaker and this information can be used to make better decisions about proper drilling parameters quickly.
Therefore there is a need for an automated computer vision based technique for identifying cuttings on a belt, and estimating various features regarding their shape, size, volume and other parameters. Information from this system can be used to provide real-time information about the well-bore to the drill-team, flag unexpected changes in the particle sizes and shapes, and/or provide a long-term recording of the particle characteristics for post-drilling analyses.
There is also a need for an automated computer vision based technique for estimating the location of the fluid front on the shale shaker.
This information may also be used to optimize, improve, or adjust the shale-shaker angle (saving mud, and/or increasing efficiency); alert an operator to expected and/or unexpected changes in the cuttings volumes which may, in some cases, be indicative of hole cleaning, influx, losses, and/or other problems; and show whether or not the volume and characteristics of cuttings exiting the shaker is less than, greater than or approximately commensurate with the rate of penetration (“ROP”).